A novel domain adaption approach for neural machine translation
J Liu, X Zhang, X Tian, J Wang… - International Journal …, 2020 - inderscienceonline.com
J Liu, X Zhang, X Tian, J Wang, AK Sangaiah
International Journal of Computational Science and Engineering, 2020•inderscienceonline.comNeural machine translation has been widely adopted in modern machine translation as it
brings the state-of-the-art performance to large-scale parallel corpora. For real-world
applications, high-quality translation for text in a specific domain is crucial. However,
performances of general neural machine models drop when being applied in a specific
domain. To alleviate this issue, this paper presents a novel method of machine translation,
which explores both model fusion algorithm and logarithmic linear interpolation. The method …
brings the state-of-the-art performance to large-scale parallel corpora. For real-world
applications, high-quality translation for text in a specific domain is crucial. However,
performances of general neural machine models drop when being applied in a specific
domain. To alleviate this issue, this paper presents a novel method of machine translation,
which explores both model fusion algorithm and logarithmic linear interpolation. The method …
Neural machine translation has been widely adopted in modern machine translation as it brings the state-of-the-art performance to large-scale parallel corpora. For real-world applications, high-quality translation for text in a specific domain is crucial. However, performances of general neural machine models drop when being applied in a specific domain. To alleviate this issue, this paper presents a novel method of machine translation, which explores both model fusion algorithm and logarithmic linear interpolation. The method can improve the performance of in-domain translation model, while preserving or even improving the performance of out-domain translation model. This paper has carried out extensive experiments on proposed translation model using the public United Nations corpus. The bilingual evaluation understudy (BLEU) score of the in-domain corpus and the out-domain corpus reaches 30.27 and 43.17 respectively, which shows a certain improvement over existing methods.

Showing the best result for this search. See all results